Application files

app.py

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+import streamlit as st
+import torch
+from tqdm import tqdm
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+
+config = PeftConfig.from_pretrained("NursNurs/T5ForReverseDictionary")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
+model = PeftModel.from_pretrained(model, "NursNurs/T5ForReverseDictionary")
+
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def return_top_k(sentence, k=10):
+
+  inputs = [f"Descripton : {sentence}. Word : "]
+
+  inputs = tokenizer(
+      inputs,
+      padding=True, truncation=True,
+      return_tensors="pt",
+  )
+
+
+  model.to(device)
+
+  with torch.no_grad():
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    output_sequences = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10, num_beams=k, num_return_sequences=k, #max_length=3,
+                                              top_p = 50, output_scores=True, return_dict_in_generate=True) #repetition_penalty=10000.0
+    #print("output_sequences", output_sequences)
+    logits = output_sequences['sequences_scores'].clone().detach()
+    decoded_probabilities = torch.softmax(logits, dim=0)
+
+
+    #all word predictions
+    predictions = [tokenizer.decode(tokens, skip_special_tokens=True) for tokens in output_sequences['sequences']]
+    probabilities = [round(float(prob), 2) for prob in decoded_probabilities]
+
+  return predictions
+
+
+st.title("You name it!")
+
+# adding the text that will show in the text box as default
+default_value = "Type the description of the word you have in mind!"
+
+sent = st.text_area("Text", default_value, height = 275)
+
+result = return_top_k(sent)
+st.write("Here are my guesses about your word:")
+st.write(result)
+